Due to the noisy and nonlinear nature of perceptual speech quality, deep learning-based data-driven approaches to speech quality assessment have moved to the forefront of non-intrusive speech quality research. Recent work has tackled the problem with several neural network designs. For instance, MOSNet [1]} uses a convolutional neural network (CNN) followed by a BLSTM to predict both frame level and utterance level scores. DNSMOS [2]} uses a self teaching [3]} approach to train a CNN-based model. AutoMOS [4]} combines a classification loss with a regression loss to predict more accurate MOS scores. NISQA
(v2.0) [5]} uses a CNN to extract frame-wise features and attention model to estimate the MOS score.
